Develop and test strategic decisions

Make sense of data

When you’re analyzing data for your organization, you need to make decisions each day: some are simple; others may be more complex. Here is a framework that can help you inform a strategic direction with data:

Know what question you’re trying to answer, or define the problem you’re trying to solve.

Look at your data to measure your baseline and benchmark where you currently are. From there you can determine whether performance improved or declined as a result of actions you took.

Decide how long to run the experiment, and implement adjustments to your creative or promotional strategies.

Once a meaningful period of time has passed, you can evaluate the success of your decision with data.

For example, if you’re trying to determine whether or not displaying non-skippable ads on your videos will increase revenue, first find out what your revenue and watch time has been for the last three months. Then enable the ad type. Recording a starting point can tell you whether or not they boosted revenue.

Group data for analysis

Grouping data allows you to organize and aggregate information into more useful sets. If you want to compare how well a singer’s new track is performing compared to other songs on the album, you may want to create a group by album. If you aggregate all of her songs by album, you can then more easily find out how the new album is performing in its first week compared to her last three albums.

You can group assets by language or on-camera personality; topical groups, like football or skateboarding may help you decide whether to spin those videos out into their own channel. Apply asset labels to create groups, or manually aggregate groups in Analytics.

There are several options outside of Content Manager with downloadable reports. You can pull CSVs into a spreadsheet, and use functions like “SORT” or “GROUP” to surface useful data. In a database language like SQL, statements like SELECT and WHERE can create manageable datasets by setting conditions that information has to meet in order to be included in a solution.

YouTube Analytics allows you to perform some basic analyses directly in the interface. You can manually aggregate groups of up to 200 assets, videos, playlists, or channels.

Simplify with filters

One of the most helpful things to do when you need to answer a question is to first focus your data so it only shows the information you need to answer your question. When you’re only looking at relevant data, it becomes easier to see what really matters.

For example, if you’re asked to determine whether a video is likely to be more popular with middle-school or high-school aged females, one of your very first steps should be to rule out male viewing trends from the dataset, and you can do this with filters.

In the Analytics interface, you can filter data by uploader, viewer type, content type, and viewing platform.

If you need additional ways to view the data for your analysis, you can use downloadable reports. The downloadable Video report includes fields such as video length, view count, comment count, whether comments are enabled, whether ads are enabled—and which formats. The Video report also includes the powerful fields video_id, asset_id, and claim_id. You can use field names to connect data from other downloadable reports so they can be correlated with one another.

You can filter information from downloadable reports with a spreadsheet, a database language like SQL, or in statistical packages. That’s where you gain a wider range of incredibly actionable information, and can filter by country, language spoken, ad formats, time of day videos are uploaded—any field in any downloadable report.

Tips

Compare information to determine causation

Data comparisons are a powerful tool. You can compare audience engagement with videos from different creators, compare channel performance over time, or compare revenue across assets. Comparisons are really good at answering common “why” questions:

To identify similar videos that performed well in the past, compare the Traffic Sources, Playback Locations, and Demographics reports. In each of these three reports, specifically look for where data about the video you’re investigating is different from data for earlier videos to answer the following questions:

Traffic Sources report

Playback Locations report

Demographics report

Why are metrics lower than expected?

Previous videos could have been picked up by a popular blog; this one hasn’t yet had this exposure.

If a video is frequently embedded in other websites, those websites may not be whitelisted to display ads.

If viewership changed geographically, ad rates may be different. There are some countries where ads can’t be shown.

Why have metrics spiked or declined?

People may be finding (or not finding) your content on different parts of the internet than previously.

Check to see if the ad formats enabled on your content have been changed recently.

Your video may have gained popularity in a country with very high/low ad rates.